Two-mode clustering methods: astructuredoverview

Mechelen, Iven Van; Bock, Hans‐Hermann; Boeck, Paul De

doi:10.1191/0962280204sm373ra

Cited by 178 publications

(87 citation statements)

References 64 publications

(89 reference statements)

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“…A good example are biclustering applications (see e.g [27,28]), which provide multiple opportunities for applying speculative threading approaches, such as competitive or-parallelism. Most instances of the biclustering problem are NP-hard.…”

Section: Potential Application Areasmentioning

confidence: 99%

High Level Thread-Based Competitive Or-Parallelism in Logtalk

Moura

Rocha

Madeira

2008

Lecture Notes in Computer Science

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Abstract. This paper presents the logic programming concept of threadbased competitive or-parallelism, which combines the original idea of competitive or-parallelism with committed-choice nondeterminism and speculative threading. In thread-based competitive or-parallelism, an explicit disjunction of subgoals is interpreted as a set of concurrent alternatives, each running in its own thread. The individual subgoals usually correspond to predicates implementing different procedures that, depending on the problem specifics, are expected to either fail or succeed with different performance levels. The subgoals compete for providing an answer and the first successful subgoal leads to the termination of the remaining ones. We discuss the implementation of thread-based competitive or-parallelism in the context of Logtalk, an object-oriented logic programming language, and present experimental results.

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Section: Potential Application Areasmentioning

confidence: 99%

High Level Thread-Based Competitive Or-Parallelism in Logtalk

Moura

Rocha

Madeira

2008

Lecture Notes in Computer Science

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“…Note that other methods for identifying overlapping object and variable clusterings have been proposed by Hartigan (1976), Mirkin et al (1995), Greenacre (1988), and Eckes and Orlik (1993). These methods, however, do not imply the optimization of an overall (least squares) loss function or do not fit the explicit model structure in (1) to the data; therefore, they will not be further considered in this manuscript (for more information regarding different classes of modeling and optimization techniques for cluster analysis, see Van Mechelen, Bock, and De Boeck 2004). Also the algorithms of De Sarbo (1982) and Mirkin et al (1995) will not be further considered, because they have been developed for fitting constrained versions of (1) to the data, implying a symmetric and/or diagonal W. long to the broader class of block-relaxation algorithms (de Leeuw 1984), the model parameters are grouped (e.g., partitioned) into a number of subsets; during the algorithmic process each subset is conditionally re-estimated in turn while keeping the parameters not belonging to the set in question fixed; this updating procedure is continued until there is no further improvement in the loss function value.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of biclustering methods has already been proposed in the literature (for an overview, see Madeira and Oliveira 2004;Van Mechelen, Bock and De Boeck 2004). Quite a few applications can be accounted for by structural mechanisms that can be formalized in terms of overlapping biclusters, which further also implies overlapping clusterings of the objects and the variables.…”

Section: Introductionmentioning

confidence: 99%

Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms

Wilderjans

Depril²,

Mechelen

2013

J Classif

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Abstract:The additive biclustering model for two-way two-mode object by variable data implies overlapping clusterings of both the objects and the variables together with a weight for each bicluster (i.e., a pair of an object and a variable cluster). In the data analysis, an additive biclustering model is fitted to given data by means of minimizing a least squares loss function. To this end, two alternating least squares algorithms (ALS) may be used: (1) P ENCLUS, and (2) Baier's ALS approach. However, both algorithms suffer from some inherent limitations, which may hamper their performance. As a way out, based on theoretical results regarding optimally designing ALS algorithms, in this paper a new ALS algorithm will be presented. In a simulation study this algorithm will be shown to outperform the existing ALS approaches.

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“…A earliest co-clustering formulation called direct clustering was introduced by Hartigan [7] who proposed a greedy algorithm for hierarchical co-clustering. We can also mention the following works [8,9,10,11,12] and the reviews in [13,14,15]. These methods are dedicated to a simultaneous clustering but not to visualisation.…”

Section: Introductionmentioning

confidence: 99%

Generalized topographic block model

Priam

Nadif²,

Govaert

2016

Neurocomputing

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Co-clustering leads to parsimony in data visualisation with a number of parameters dramatically reduced in comparison to the dimensions of the data sample. Herein, we propose a new generalized approach for nonlinear mapping by a re-parameterization of the latent block mixture model. The densities modeling the blocks are in an exponential family such that the Gaussian, Bernoulli and Poisson laws are particular cases. The inference of the parameters is derived from the block expectation-maximization algorithm with a Newton-Raphson procedure at the maximization step. Empirical experiments with textual data validate the interest of our generalized model.The authors have very carefully revised the document by following every comments from the editors and the two anonymous reviewers. It has been tried every possible effort to solve each remark that has been addressed. Below a detailed summary of the updates is provided. We would like to thank the editors and the reviewers for their constructive comments on this manuscript and positive support.To Editors:Once more, we would very much like to invite you to revise your paper, seriously taking into account the comments of the reviewers, and to resubmit your revised version by 02/25/2015 (mm/dd/yy). Any revision received after that may be treated as a new submission. Authors' response:The paper has been revised according to the comments and suggestions of reviewer 2.2) To Reviewer #1:The revised manuscript is sufficient to Neurocomputing publication standards, and I suggest accepting this manuscript. Authors' response:Thanks for the positive comments and the opportunity of publishing the document in Neurocomputing.3) To Reviewer #2: Q1. I thank the authors for the revised version of their manuscript. Authors' response:Thanks for the positive comments.Q2. They open the abstract with the statement: "Parametric methods for data visualisation are most of the time founded on an usual mixture model framework." Even a light-hearted revision of existing parametric methods for multivariate data visualization (See, for instance, Lee & Verleysen, 2007) would reveal that this is not the case. Therefore, I think this statement should be either removed or revised. Authors' response:Thanks for this suggestion. Indeed, the term « parametric methods » was meaning « probabilistic methods » or « parametric model » in a statistical framework and could have been read as any methods with parameters on the contrary to svd for instance. This sentence has been removed, and the summary updated for complying with other comments in the review.Q3. Co(Bi)-clustering in general and co(bi)-clustering with visualization-oriented self-organizing models are more adequately introduced in the new version. Authors' response:Thanks for this remark.Q4. I am a bit puzzled by the new introduction "storyline", though. It roughly goes like this:Revision Notes a) -Co-clustering was first proposed in the seventies and some more works [6][7][8][9][10][11] Authors' response:Thanks for this concern. Indeed, af...

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Two-mode clustering methods: astructuredoverview

Cited by 178 publications

References 64 publications

High Level Thread-Based Competitive Or-Parallelism in Logtalk

High Level Thread-Based Competitive Or-Parallelism in Logtalk

Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms

Generalized topographic block model

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